Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 32

Throughput images/s, Multi Node

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Deep Learning Performance: Scale-up vs Scale-out 7.2 Throughput images/s - Multi Node 7.2.1 PowerEdge C4130-P100 16GB PCIe- Multi Node PowerEdge C4130 each with 4 P100-PCIe GPUs were configured in multi-node using InfiniBand RDMA to run the TensorFlow in distributed mode. Figure 25: Training with PowerEdge C4130-P100-16GB-PCle in multi-node PowerEdge C4130 server scales very well within a node with 97% efficiency and 92% across the nodes. The ideal performance is computed by multiplying the single-GPU throughput by the number of GPUs in the system. See Figure 26. Architectures & Technologies Dell EMC | Infrastructure Solutions Group 31

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Deep Learning Performance: Scale-up vs Scale-out
Architectures & Technologies
Dell
EMC
| Infrastructure Solutions Group
31
7.2
Throughput images/s
–
Multi Node
7.2.1
PowerEdge C4130-P100 16GB PCIe- Multi Node
PowerEdge C4130 each with 4 P100-PCIe GPUs were configured in multi-node using InfiniBand
RDMA to run the TensorFlow in distributed mode.
Figure 25: Training with PowerEdge C4130-P100-16GB-PCle in multi-node
PowerEdge C4130 server scales very well within a node with 97% efficiency and 92% across the
nodes. The ideal performance is computed by multiplying the single-GPU throughput by the
number of GPUs in the system. See
Figure 26
.